AI 中文总结
长格式问答中存在引用不足以支持主张的问题，CAGE提出两阶段框架，先训练认知图归纳模型构建支持子图，再用结构化引用推理模型生成对齐引用，实验证明该方法有效并实现最优性能。
AI 中文摘要
长格式问答越来越依赖检索到的证据以使大语言模型输出可验证，通过内联引用将主张追溯到源文档。然而，现有系统经常附加主题相关但不足以支持其主张的引用。我们将归因模糊性识别为一个结构挑战：端到端生成必须隐含地解决组合式主张与文档的分配问题，模糊证据边界并增加证据边界超限风险。为应对这一挑战，我们提出CAGE，一个在答案生成前引入显式认知归因图的两阶段框架。CAGE首先训练一个即插即用的认知图归纳模型来构建以答案为中心的支持子图，通过显式关系将每个语义答案单元与支持文档对齐。然后一个结构化引用推理模型将这些单元实现为具有图对齐引用的句子级主张。在ASQA、ELI5和ExpertQA上的实验表明CAGE实现了最优性能，证明了归因空间收缩和图引导引用生成的有效性。
英文摘要
Long-form question answering increasingly relies on retrieved evidence to make LLM outputs verifiable, with inline citations tracing claims to source documents. However, existing systems often attach citations that are topically related but insufficient to support their claims. We identify attribution ambiguity as a structural challenge: end-to-end generation must implicitly resolve combinatorial claim--document assignments, obscuring evidential boundaries and increasing the risk of evidence-boundary overrun, where claims exceed cited support. To address this challenge, we propose CAGE (Cognitive Attribution Graphs for Citation Generation), a two-stage framework that introduces an explicit cognitive attribution map before answer generation. CAGE first trains a plug-and-play Cognitive Map Induction Model to construct answer-centered support subgraphs, aligning each semantic answer unit with supporting documents through explicit relations. A Structured Citation Reasoning Model then realizes these units as sentence-level claims with map-aligned citations. Experiments on ASQA, ELI5, and ExpertQA show that CAGE achieves state-of-the-art performance, demonstrating the effectiveness of attribution-space contraction and map-guided citation generation.
